Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations733863
Missing cells28
Missing cells (%)< 0.1%
Total size in memory552.6 MiB
Average record size in memory789.6 B

Variable types

Numeric18
Text11
Boolean1

Alerts

is_anomaly is highly imbalanced (88.3%) Imbalance
subscriber_sim_count is highly skewed (γ1 = 27.53600142) Skewed
pdv_avg_sims_per_customer is highly skewed (γ1 = 20.48589947) Skewed
reconstruction_error is highly skewed (γ1 = 28.40803767) Skewed
msisdn has unique values Unique
activation_dayofweek has 109831 (15.0%) zeros Zeros
same_wilaya has 68752 (9.4%) zeros Zeros
wilaya_code_diff has 665111 (90.6%) zeros Zeros
days_since_pdv_first_activation has 15549 (2.1%) zeros Zeros
time_phase_code has 127073 (17.3%) zeros Zeros
pdv_multi_sim_pct has 24230 (3.3%) zeros Zeros

Reproduction

Analysis started2025-04-28 00:04:10.687247
Analysis finished2025-04-28 00:04:30.075565
Duration19.39 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

msisdn
Real number (ℝ)

Unique 

Distinct733863
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324843728.4
Minimum300010025
Maximum349740621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:30.199351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum300010025
5-th percentile302588400.4
Q1312427645
median324867641
Q3337367331
95-th percentile347287608.9
Maximum349740621
Range49730596
Interquartile range (IQR)24939686

Descriptive statistics

Standard deviation14373082.22
Coefficient of variation (CV)0.0442461435
Kurtosis-1.209513558
Mean324843728.4
Median Absolute Deviation (MAD)12469377
Skewness0.007568724491
Sum2.38390793 × 1014
Variance2.065854926 × 1014
MonotonicityStrictly increasing
2025-04-28T01:04:30.284124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
349740621 1
 
< 0.1%
300010025 1
 
< 0.1%
300010038 1
 
< 0.1%
300010052 1
 
< 0.1%
300010075 1
 
< 0.1%
300010082 1
 
< 0.1%
300010113 1
 
< 0.1%
349740378 1
 
< 0.1%
349740372 1
 
< 0.1%
349740371 1
 
< 0.1%
Other values (733853) 733853
> 99.9%
ValueCountFrequency (%)
300010025 1
< 0.1%
300010038 1
< 0.1%
300010052 1
< 0.1%
300010075 1
< 0.1%
300010082 1
< 0.1%
ValueCountFrequency (%)
349740621 1
< 0.1%
349740612 1
< 0.1%
349740608 1
< 0.1%
349740602 1
< 0.1%
349740574 1
< 0.1%
Distinct666321
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size47.6 MiB
2025-04-28T01:04:30.587002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters13943397
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique604980 ?
Unique (%)82.4%

Sample

1st row2025-01-29 10:56:38
2nd row2025-03-06 11:18:09
3rd row2025-03-14 22:48:23
4th row2025-02-22 11:54:44
5th row2025-02-02 14:36:33
ValueCountFrequency (%)
2025-02-27 14715
 
1.0%
2025-01-30 14006
 
1.0%
2025-02-28 12739
 
0.9%
2025-03-30 12693
 
0.9%
2025-02-20 12313
 
0.8%
2025-03-29 12229
 
0.8%
2025-01-29 11965
 
0.8%
2025-02-26 11711
 
0.8%
2025-04-05 10669
 
0.7%
2025-02-16 10425
 
0.7%
Other values (62929) 1344261
91.6%
2025-04-28T01:04:31.377953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2663710
19.1%
0 2273812
16.3%
1 1540589
11.0%
- 1467726
10.5%
: 1467726
10.5%
5 1262125
9.1%
3 792468
 
5.7%
733863
 
5.3%
4 560640
 
4.0%
7 304087
 
2.2%
Other values (3) 876651
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13943397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2663710
19.1%
0 2273812
16.3%
1 1540589
11.0%
- 1467726
10.5%
: 1467726
10.5%
5 1262125
9.1%
3 792468
 
5.7%
733863
 
5.3%
4 560640
 
4.0%
7 304087
 
2.2%
Other values (3) 876651
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13943397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2663710
19.1%
0 2273812
16.3%
1 1540589
11.0%
- 1467726
10.5%
: 1467726
10.5%
5 1262125
9.1%
3 792468
 
5.7%
733863
 
5.3%
4 560640
 
4.0%
7 304087
 
2.2%
Other values (3) 876651
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13943397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2663710
19.1%
0 2273812
16.3%
1 1540589
11.0%
- 1467726
10.5%
: 1467726
10.5%
5 1262125
9.1%
3 792468
 
5.7%
733863
 
5.3%
4 560640
 
4.0%
7 304087
 
2.2%
Other values (3) 876651
 
6.3%
Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.0 MiB
2025-04-28T01:04:31.685552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length18
Mean length6.745381631
Min length1

Characters and Unicode

Total characters4950186
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)< 0.1%

Sample

1st rowALGER
2nd rowOULED-DJELLAL
3rd rowCONSTANTINE
4th rowOUARGLA
5th rowBORDJ-BOU-ARRERIDJ
ValueCountFrequency (%)
alger 144198
19.4%
constantine 52621
 
7.1%
setif 52303
 
7.0%
oran 35823
 
4.8%
chlef 34079
 
4.6%
blida 32901
 
4.4%
msila 30845
 
4.1%
djelfa 30188
 
4.1%
boumerdes 26949
 
3.6%
medea 20921
 
2.8%
Other values (102) 282960
38.0%
2025-04-28T01:04:32.147887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 701683
14.2%
E 628949
12.7%
L 420535
 
8.5%
I 356128
 
7.2%
R 325070
 
6.6%
N 289129
 
5.8%
T 260154
 
5.3%
S 245806
 
5.0%
O 233486
 
4.7%
D 211222
 
4.3%
Other values (39) 1278024
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4950186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 701683
14.2%
E 628949
12.7%
L 420535
 
8.5%
I 356128
 
7.2%
R 325070
 
6.6%
N 289129
 
5.8%
T 260154
 
5.3%
S 245806
 
5.0%
O 233486
 
4.7%
D 211222
 
4.3%
Other values (39) 1278024
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4950186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 701683
14.2%
E 628949
12.7%
L 420535
 
8.5%
I 356128
 
7.2%
R 325070
 
6.6%
N 289129
 
5.8%
T 260154
 
5.3%
S 245806
 
5.0%
O 233486
 
4.7%
D 211222
 
4.3%
Other values (39) 1278024
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4950186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 701683
14.2%
E 628949
12.7%
L 420535
 
8.5%
I 356128
 
7.2%
R 325070
 
6.6%
N 289129
 
5.8%
T 260154
 
5.3%
S 245806
 
5.0%
O 233486
 
4.7%
D 211222
 
4.3%
Other values (39) 1278024
25.8%
Distinct610804
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size46.4 MiB
2025-04-28T01:04:32.902821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length18
Mean length17.36415653
Min length1

Characters and Unicode

Total characters12742912
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique518154 ?
Unique (%)70.6%

Sample

1st row069182991590951419
2nd row59048923899923990
3rd row109778991590927189
4th row715903991550999329
5th row965546992559917639
ValueCountFrequency (%)
959293 905
 
0.1%
993453 264
 
< 0.1%
5 144
 
< 0.1%
798154 68
 
< 0.1%
049316997590954889 50
 
< 0.1%
919316998299921489 46
 
< 0.1%
829302996590929029 44
 
< 0.1%
sj13319 39
 
< 0.1%
hd/0099/639 39
 
< 0.1%
959372992599995539 38
 
< 0.1%
Other values (610896) 732637
99.8%
2025-04-28T01:04:33.732597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 5015301
39.4%
5 1800804
 
14.1%
0 1242655
 
9.8%
7 753631
 
5.9%
2 723438
 
5.7%
8 692186
 
5.4%
1 679700
 
5.3%
4 612600
 
4.8%
3 604036
 
4.7%
6 585516
 
4.6%
Other values (58) 33045
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12742912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 5015301
39.4%
5 1800804
 
14.1%
0 1242655
 
9.8%
7 753631
 
5.9%
2 723438
 
5.7%
8 692186
 
5.4%
1 679700
 
5.3%
4 612600
 
4.8%
3 604036
 
4.7%
6 585516
 
4.6%
Other values (58) 33045
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12742912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 5015301
39.4%
5 1800804
 
14.1%
0 1242655
 
9.8%
7 753631
 
5.9%
2 723438
 
5.7%
8 692186
 
5.4%
1 679700
 
5.3%
4 612600
 
4.8%
3 604036
 
4.7%
6 585516
 
4.6%
Other values (58) 33045
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12742912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 5015301
39.4%
5 1800804
 
14.1%
0 1242655
 
9.8%
7 753631
 
5.9%
2 723438
 
5.7%
8 692186
 
5.4%
1 679700
 
5.3%
4 612600
 
4.8%
3 604036
 
4.7%
6 585516
 
4.6%
Other values (58) 33045
 
0.3%

pdv_id
Text

Distinct12689
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size39.0 MiB
2025-04-28T01:04:34.328954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.69813303
Min length1

Characters and Unicode

Total characters4915512
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique646 ?
Unique (%)0.1%

Sample

1st rowD48
2nd rowP347521
3rd rowP665565
4th rowP966630
5th rowP954533
ValueCountFrequency (%)
p326910 5523
 
0.8%
p144920 4842
 
0.7%
p948936 4384
 
0.6%
dh7 3466
 
0.5%
p193926 3179
 
0.4%
p896969 3069
 
0.4%
p287979 2941
 
0.4%
p240569 2855
 
0.4%
p839536 2840
 
0.4%
p544533 2835
 
0.4%
Other values (12664) 698483
95.1%
2025-04-28T01:04:34.761973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 678957
13.8%
P 673382
13.7%
9 636276
12.9%
6 491065
10.0%
3 486653
9.9%
2 444890
9.1%
4 305566
6.2%
8 298483
6.1%
7 278437
5.7%
0 275557
5.6%
Other values (12) 346246
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4915512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 678957
13.8%
P 673382
13.7%
9 636276
12.9%
6 491065
10.0%
3 486653
9.9%
2 444890
9.1%
4 305566
6.2%
8 298483
6.1%
7 278437
5.7%
0 275557
5.6%
Other values (12) 346246
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4915512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 678957
13.8%
P 673382
13.7%
9 636276
12.9%
6 491065
10.0%
3 486653
9.9%
2 444890
9.1%
4 305566
6.2%
8 298483
6.1%
7 278437
5.7%
0 275557
5.6%
Other values (12) 346246
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4915512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 678957
13.8%
P 673382
13.7%
9 636276
12.9%
6 491065
10.0%
3 486653
9.9%
2 444890
9.1%
4 305566
6.2%
8 298483
6.1%
7 278437
5.7%
0 275557
5.6%
Other values (12) 346246
7.0%
Distinct106
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.1 MiB
2025-04-28T01:04:34.914494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length19
Mean length9.423129385
Min length1

Characters and Unicode

Total characters6915286
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAlgiers
2nd row07-BISKRA
3rd row25-CONSTANTINE
4th row30-OUARGLA
5th row34-BORDJ-BOU-ARRERIDJ
ValueCountFrequency (%)
16-alger 134911
18.0%
19-setif 50086
 
6.7%
25-constantine 49873
 
6.6%
09-blida 33434
 
4.5%
02-chlef 31829
 
4.2%
31-oran 31799
 
4.2%
35-boumerdes 30294
 
4.0%
17-djelfa 30082
 
4.0%
28-msila 29251
 
3.9%
26-medea 19721
 
2.6%
Other values (109) 309022
41.2%
2025-04-28T01:04:35.180672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 755989
 
10.9%
A 650574
 
9.4%
E 570754
 
8.3%
L 355952
 
5.1%
I 330398
 
4.8%
R 309978
 
4.5%
1 307964
 
4.5%
N 261532
 
3.8%
S 242811
 
3.5%
T 238895
 
3.5%
Other values (52) 2890439
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6915286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 755989
 
10.9%
A 650574
 
9.4%
E 570754
 
8.3%
L 355952
 
5.1%
I 330398
 
4.8%
R 309978
 
4.5%
1 307964
 
4.5%
N 261532
 
3.8%
S 242811
 
3.5%
T 238895
 
3.5%
Other values (52) 2890439
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6915286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 755989
 
10.9%
A 650574
 
9.4%
E 570754
 
8.3%
L 355952
 
5.1%
I 330398
 
4.8%
R 309978
 
4.5%
1 307964
 
4.5%
N 261532
 
3.8%
S 242811
 
3.5%
T 238895
 
3.5%
Other values (52) 2890439
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6915286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 755989
 
10.9%
A 650574
 
9.4%
E 570754
 
8.3%
L 355952
 
5.1%
I 330398
 
4.8%
R 309978
 
4.5%
1 307964
 
4.5%
N 261532
 
3.8%
S 242811
 
3.5%
T 238895
 
3.5%
Other values (52) 2890439
41.8%

status
Text

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.5 MiB
2025-04-28T01:04:35.266636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length6
Mean length6.06400786
Min length4

Characters and Unicode

Total characters4450151
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowActive
2nd rowActive
3rd rowActive
4th rowActive
5th rowActive
ValueCountFrequency (%)
active 716611
97.6%
suspended 15897
 
2.2%
idle 1023
 
0.1%
terminated 332
 
< 0.1%
2025-04-28T01:04:35.431999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 750092
16.9%
t 716943
16.1%
i 716943
16.1%
A 716611
16.1%
c 716611
16.1%
v 716611
16.1%
d 33149
 
0.7%
n 16229
 
0.4%
S 15897
 
0.4%
u 15897
 
0.4%
Other values (8) 35168
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4450151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 750092
16.9%
t 716943
16.1%
i 716943
16.1%
A 716611
16.1%
c 716611
16.1%
v 716611
16.1%
d 33149
 
0.7%
n 16229
 
0.4%
S 15897
 
0.4%
u 15897
 
0.4%
Other values (8) 35168
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4450151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 750092
16.9%
t 716943
16.1%
i 716943
16.1%
A 716611
16.1%
c 716611
16.1%
v 716611
16.1%
d 33149
 
0.7%
n 16229
 
0.4%
S 15897
 
0.4%
u 15897
 
0.4%
Other values (8) 35168
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4450151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 750092
16.9%
t 716943
16.1%
i 716943
16.1%
A 716611
16.1%
c 716611
16.1%
v 716611
16.1%
d 33149
 
0.7%
n 16229
 
0.4%
S 15897
 
0.4%
u 15897
 
0.4%
Other values (8) 35168
 
0.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.3 MiB
2025-04-28T01:04:35.498383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length10
Mean length9.994105167
Min length8

Characters and Unicode

Total characters7334304
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowINDIVIDUAL
4th rowINDIVIDUAL
5th rowINDIVIDUAL
ValueCountFrequency (%)
individual 731547
99.7%
business 2265
 
0.3%
non 51
 
< 0.1%
commercial 51
 
< 0.1%
2025-04-28T01:04:35.668825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 2196957
30.0%
D 1463094
19.9%
N 733914
 
10.0%
U 733812
 
10.0%
A 731598
 
10.0%
L 731598
 
10.0%
V 731547
 
10.0%
S 6795
 
0.1%
E 2316
 
< 0.1%
B 2265
 
< 0.1%
Other values (5) 408
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7334304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 2196957
30.0%
D 1463094
19.9%
N 733914
 
10.0%
U 733812
 
10.0%
A 731598
 
10.0%
L 731598
 
10.0%
V 731547
 
10.0%
S 6795
 
0.1%
E 2316
 
< 0.1%
B 2265
 
< 0.1%
Other values (5) 408
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7334304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 2196957
30.0%
D 1463094
19.9%
N 733914
 
10.0%
U 733812
 
10.0%
A 731598
 
10.0%
L 731598
 
10.0%
V 731547
 
10.0%
S 6795
 
0.1%
E 2316
 
< 0.1%
B 2265
 
< 0.1%
Other values (5) 408
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7334304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 2196957
30.0%
D 1463094
19.9%
N 733914
 
10.0%
U 733812
 
10.0%
A 731598
 
10.0%
L 731598
 
10.0%
V 731547
 
10.0%
S 6795
 
0.1%
E 2316
 
< 0.1%
B 2265
 
< 0.1%
Other values (5) 408
 
< 0.1%
Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.5 MiB
2025-04-28T01:04:35.752595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length1
Mean length1.773027118
Min length1

Characters and Unicode

Total characters1301159
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?
ValueCountFrequency (%)
634083
86.0%
alger 25436
 
3.5%
setif 7169
 
1.0%
constantine 6526
 
0.9%
djelfa 6021
 
0.8%
blida 4293
 
0.6%
boumerdes 3917
 
0.5%
oran 3702
 
0.5%
medea 3228
 
0.4%
msila 2649
 
0.4%
Other values (56) 40228
 
5.5%
2025-04-28T01:04:35.955964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 634083
48.7%
A 97479
 
7.5%
E 87608
 
6.7%
L 59458
 
4.6%
R 45555
 
3.5%
I 44174
 
3.4%
N 35373
 
2.7%
T 33594
 
2.6%
S 32778
 
2.5%
G 31324
 
2.4%
Other values (20) 199733
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1301159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 634083
48.7%
A 97479
 
7.5%
E 87608
 
6.7%
L 59458
 
4.6%
R 45555
 
3.5%
I 44174
 
3.4%
N 35373
 
2.7%
T 33594
 
2.6%
S 32778
 
2.5%
G 31324
 
2.4%
Other values (20) 199733
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1301159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 634083
48.7%
A 97479
 
7.5%
E 87608
 
6.7%
L 59458
 
4.6%
R 45555
 
3.5%
I 44174
 
3.4%
N 35373
 
2.7%
T 33594
 
2.6%
S 32778
 
2.5%
G 31324
 
2.4%
Other values (20) 199733
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1301159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 634083
48.7%
A 97479
 
7.5%
E 87608
 
6.7%
L 59458
 
4.6%
R 45555
 
3.5%
I 44174
 
3.4%
N 35373
 
2.7%
T 33594
 
2.6%
S 32778
 
2.5%
G 31324
 
2.4%
Other values (20) 199733
 
15.4%
Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.5 MiB
2025-04-28T01:04:36.082305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length18
Mean length6.790851971
Min length1

Characters and Unicode

Total characters4983555
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowAlger
2nd rowOuled Djellal
3rd rowConstantine
4th rowOuargla
5th rowBordj Bou Arreridj
ValueCountFrequency (%)
alger 144199
 
17.1%
constantine 52621
 
6.3%
sétif 52303
 
6.2%
oran 35823
 
4.3%
chlef 34079
 
4.1%
blida 32901
 
3.9%
m'sila 30845
 
3.7%
djelfa 30188
 
3.6%
boumerdès 26949
 
3.2%
aïn 24483
 
2.9%
Other values (73) 376700
44.8%
2025-04-28T01:04:36.294344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 505065
 
10.1%
e 450814
 
9.0%
l 418966
 
8.4%
r 315298
 
6.3%
i 308878
 
6.2%
n 288644
 
5.8%
t 205383
 
4.1%
A 196766
 
3.9%
g 171109
 
3.4%
o 159613
 
3.2%
Other values (42) 1963019
39.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4983555
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 505065
 
10.1%
e 450814
 
9.0%
l 418966
 
8.4%
r 315298
 
6.3%
i 308878
 
6.2%
n 288644
 
5.8%
t 205383
 
4.1%
A 196766
 
3.9%
g 171109
 
3.4%
o 159613
 
3.2%
Other values (42) 1963019
39.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4983555
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 505065
 
10.1%
e 450814
 
9.0%
l 418966
 
8.4%
r 315298
 
6.3%
i 308878
 
6.2%
n 288644
 
5.8%
t 205383
 
4.1%
A 196766
 
3.9%
g 171109
 
3.4%
o 159613
 
3.2%
Other values (42) 1963019
39.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4983555
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 505065
 
10.1%
e 450814
 
9.0%
l 418966
 
8.4%
r 315298
 
6.3%
i 308878
 
6.2%
n 288644
 
5.8%
t 205383
 
4.1%
A 196766
 
3.9%
g 171109
 
3.4%
o 159613
 
3.2%
Other values (42) 1963019
39.4%
Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.4 MiB
2025-04-28T01:04:36.409442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length18
Mean length6.671701666
Min length4

Characters and Unicode

Total characters4896115
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAlger
2nd rowBiskra
3rd rowConstantine
4th rowOuargla
5th rowBordj Bou Arreridj
ValueCountFrequency (%)
alger 148743
18.0%
constantine 53152
 
6.4%
sétif 52240
 
6.3%
oran 37792
 
4.6%
blida 35641
 
4.3%
chlef 32804
 
4.0%
djelfa 30747
 
3.7%
boumerdès 30740
 
3.7%
m'sila 30197
 
3.6%
aïn 23591
 
2.8%
Other values (60) 352474
42.6%
2025-04-28T01:04:36.598579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 498864
 
10.2%
e 434126
 
8.9%
l 384269
 
7.8%
r 330065
 
6.7%
i 311362
 
6.4%
n 288294
 
5.9%
t 202345
 
4.1%
A 199680
 
4.1%
g 174423
 
3.6%
B 164938
 
3.4%
Other values (37) 1907749
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4896115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 498864
 
10.2%
e 434126
 
8.9%
l 384269
 
7.8%
r 330065
 
6.7%
i 311362
 
6.4%
n 288294
 
5.9%
t 202345
 
4.1%
A 199680
 
4.1%
g 174423
 
3.6%
B 164938
 
3.4%
Other values (37) 1907749
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4896115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 498864
 
10.2%
e 434126
 
8.9%
l 384269
 
7.8%
r 330065
 
6.7%
i 311362
 
6.4%
n 288294
 
5.9%
t 202345
 
4.1%
A 199680
 
4.1%
g 174423
 
3.6%
B 164938
 
3.4%
Other values (37) 1907749
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4896115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 498864
 
10.2%
e 434126
 
8.9%
l 384269
 
7.8%
r 330065
 
6.7%
i 311362
 
6.4%
n 288294
 
5.9%
t 202345
 
4.1%
A 199680
 
4.1%
g 174423
 
3.6%
B 164938
 
3.4%
Other values (37) 1907749
39.0%

subscriber_wilaya_code
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21.69939865
Minimum0
Maximum99
Zeros90
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:36.701855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q116
median19
Q328
95-th percentile44
Maximum99
Range99
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.89623928
Coefficient of variation (CV)0.5482289843
Kurtosis-0.0306252935
Mean21.69939865
Median Absolute Deviation (MAD)7
Skewness0.5660107938
Sum15924082
Variance141.5205091
MonotonicityNot monotonic
2025-04-28T01:04:36.805957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 144198
19.6%
25 52621
 
7.2%
19 52303
 
7.1%
31 35823
 
4.9%
2 34079
 
4.6%
9 32901
 
4.5%
28 30845
 
4.2%
17 30188
 
4.1%
35 26949
 
3.7%
26 20921
 
2.9%
Other values (50) 273021
37.2%
ValueCountFrequency (%)
0 90
 
< 0.1%
1 1329
 
0.2%
2 34079
4.6%
3 2670
 
0.4%
4 8880
 
1.2%
ValueCountFrequency (%)
99 36
 
< 0.1%
58 306
 
< 0.1%
57 1526
0.2%
56 115
 
< 0.1%
55 1407
0.2%

pdv_wilaya_code
Real number (ℝ)

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.96676491
Minimum0
Maximum58
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:36.898223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q116
median19
Q328
95-th percentile44
Maximum58
Range58
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.17824321
Coefficient of variation (CV)0.5331410569
Kurtosis-0.2824807568
Mean20.96676491
Median Absolute Deviation (MAD)7
Skewness0.4458844056
Sum15386733
Variance124.9531212
MonotonicityNot monotonic
2025-04-28T01:04:37.006808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 148743
20.3%
25 53152
 
7.2%
19 52240
 
7.1%
31 37792
 
5.1%
9 35641
 
4.9%
2 32804
 
4.5%
17 30747
 
4.2%
35 30740
 
4.2%
28 30197
 
4.1%
26 20565
 
2.8%
Other values (48) 261242
35.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1569
 
0.2%
2 32804
4.5%
3 2732
 
0.4%
4 8699
 
1.2%
ValueCountFrequency (%)
58 189
< 0.1%
57 174
< 0.1%
56 100
< 0.1%
54 148
< 0.1%
53 126
< 0.1%

activation_hour
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.6471385
Minimum0
Maximum23
Zeros6009
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:37.109943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q112
median16
Q319
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.165197699
Coefficient of variation (CV)0.2661954899
Kurtosis0.5974994701
Mean15.6471385
Median Absolute Deviation (MAD)3
Skewness-0.5390217014
Sum11482856
Variance17.34887187
MonotonicityNot monotonic
2025-04-28T01:04:37.183580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
17 82413
11.2%
18 63913
 
8.7%
16 61173
 
8.3%
15 56332
 
7.7%
11 52814
 
7.2%
14 52074
 
7.1%
19 48665
 
6.6%
12 48062
 
6.5%
21 47198
 
6.4%
20 46981
 
6.4%
Other values (14) 174238
23.7%
ValueCountFrequency (%)
0 6009
0.8%
1 1863
 
0.3%
2 497
 
0.1%
3 93
 
< 0.1%
4 60
 
< 0.1%
ValueCountFrequency (%)
23 16554
 
2.3%
22 34329
4.7%
21 47198
6.4%
20 46981
6.4%
19 48665
6.6%

activation_dayofweek
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.009771579
Minimum0
Maximum6
Zeros109831
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:37.267906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.017180449
Coefficient of variation (CV)0.6702104783
Kurtosis-1.222791261
Mean3.009771579
Median Absolute Deviation (MAD)2
Skewness-0.001162837344
Sum2208760
Variance4.069016965
MonotonicityNot monotonic
2025-04-28T01:04:37.350101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 124607
17.0%
6 119254
16.3%
0 109831
15.0%
3 109141
14.9%
1 100860
13.7%
2 94775
12.9%
5 75395
10.3%
ValueCountFrequency (%)
0 109831
15.0%
1 100860
13.7%
2 94775
12.9%
3 109141
14.9%
4 124607
17.0%
ValueCountFrequency (%)
6 119254
16.3%
5 75395
10.3%
4 124607
17.0%
3 109141
14.9%
2 94775
12.9%

activation_month
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.195886698
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:37.417134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9192218523
Coefficient of variation (CV)0.418610784
Kurtosis-0.9318852824
Mean2.195886698
Median Absolute Deviation (MAD)1
Skewness0.1829677786
Sum1611480
Variance0.8449688137
MonotonicityNot monotonic
2025-04-28T01:04:37.500382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2 250841
34.2%
3 230774
31.4%
1 197172
26.9%
4 55076
 
7.5%
ValueCountFrequency (%)
1 197172
26.9%
2 250841
34.2%
3 230774
31.4%
4 55076
 
7.5%
ValueCountFrequency (%)
4 55076
 
7.5%
3 230774
31.4%
2 250841
34.2%
1 197172
26.9%

activation_week
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.170488225
Minimum2
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:37.569811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median8
Q311
95-th percentile14
Maximum15
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.648526519
Coefficient of variation (CV)0.4465493883
Kurtosis-1.137888382
Mean8.170488225
Median Absolute Deviation (MAD)3
Skewness0.04959208318
Sum5996019
Variance13.31174576
MonotonicityNot monotonic
2025-04-28T01:04:37.649817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
9 76252
10.4%
5 68015
9.3%
13 64004
8.7%
8 62000
8.4%
7 56999
7.8%
3 56911
7.8%
6 55974
7.6%
4 54504
7.4%
12 52688
 
7.2%
14 50933
 
6.9%
Other values (4) 135583
18.5%
ValueCountFrequency (%)
2 33993
4.6%
3 56911
7.8%
4 54504
7.4%
5 68015
9.3%
6 55974
7.6%
ValueCountFrequency (%)
15 8761
 
1.2%
14 50933
6.9%
13 64004
8.7%
12 52688
7.2%
11 45029
6.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.8 MiB
2025-04-28T01:04:37.733135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.920981164
Min length5

Characters and Unicode

Total characters5812915
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMorning
2nd rowMorning
3rd rowEvening
4th rowMorning
5th rowAfternoon
ValueCountFrequency (%)
afternoon 340539
46.4%
evening 263649
35.9%
morning 127073
 
17.3%
night 2602
 
0.4%
2025-04-28T01:04:37.897979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1462522
25.2%
o 808151
13.9%
e 604188
10.4%
r 467612
 
8.0%
i 393324
 
6.8%
g 393324
 
6.8%
t 343141
 
5.9%
f 340539
 
5.9%
A 340539
 
5.9%
E 263649
 
4.5%
Other values (4) 395926
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5812915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1462522
25.2%
o 808151
13.9%
e 604188
10.4%
r 467612
 
8.0%
i 393324
 
6.8%
g 393324
 
6.8%
t 343141
 
5.9%
f 340539
 
5.9%
A 340539
 
5.9%
E 263649
 
4.5%
Other values (4) 395926
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5812915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1462522
25.2%
o 808151
13.9%
e 604188
10.4%
r 467612
 
8.0%
i 393324
 
6.8%
g 393324
 
6.8%
t 343141
 
5.9%
f 340539
 
5.9%
A 340539
 
5.9%
E 263649
 
4.5%
Other values (4) 395926
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5812915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1462522
25.2%
o 808151
13.9%
e 604188
10.4%
r 467612
 
8.0%
i 393324
 
6.8%
g 393324
 
6.8%
t 343141
 
5.9%
f 340539
 
5.9%
A 340539
 
5.9%
E 263649
 
4.5%
Other values (4) 395926
 
6.8%

same_wilaya
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9063149389
Minimum0
Maximum1
Zeros68752
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:37.967393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2913902643
Coefficient of variation (CV)0.3215110463
Kurtosis5.777477142
Mean0.9063149389
Median Absolute Deviation (MAD)0
Skewness-2.788810032
Sum665111
Variance0.08490828613
MonotonicityNot monotonic
2025-04-28T01:04:38.049475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 665111
90.6%
0 68752
 
9.4%
ValueCountFrequency (%)
0 68752
 
9.4%
1 665111
90.6%
ValueCountFrequency (%)
1 665111
90.6%
0 68752
 
9.4%

wilaya_code_diff
Real number (ℝ)

Zeros 

Distinct64
Distinct (%)< 0.1%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.816282369
Minimum0
Maximum87
Zeros665111
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:38.116507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15
Maximum87
Range87
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.091319635
Coefficient of variation (CV)3.904304616
Kurtosis21.90790621
Mean1.816282369
Median Absolute Deviation (MAD)0
Skewness4.591364429
Sum1332877
Variance50.28681417
MonotonicityNot monotonic
2025-04-28T01:04:38.223476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 665111
90.6%
44 9534
 
1.3%
7 4890
 
0.7%
19 4712
 
0.6%
3 3176
 
0.4%
18 2900
 
0.4%
10 2748
 
0.4%
2 2662
 
0.4%
1 2360
 
0.3%
25 2215
 
0.3%
Other values (54) 33541
 
4.6%
ValueCountFrequency (%)
0 665111
90.6%
1 2360
 
0.3%
2 2662
 
0.4%
3 3176
 
0.4%
4 1673
 
0.2%
ValueCountFrequency (%)
87 4
< 0.1%
83 7
< 0.1%
80 6
< 0.1%
78 8
< 0.1%
71 6
< 0.1%

pdv_activation_count
Real number (ℝ)

Distinct931
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1133.254459
Minimum1
Maximum10846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:38.299922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1131
median440
Q31349
95-th percentile5350
Maximum10846
Range10845
Interquartile range (IQR)1218

Descriptive statistics

Standard deviation1796.797711
Coefficient of variation (CV)1.585520089
Kurtosis10.75911886
Mean1133.254459
Median Absolute Deviation (MAD)377
Skewness3.062936257
Sum831653517
Variance3228482.014
MonotonicityNot monotonic
2025-04-28T01:04:38.398678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10846 5523
 
0.8%
9717 4842
 
0.7%
8715 4384
 
0.6%
6864 3466
 
0.5%
6270 3179
 
0.4%
5925 3069
 
0.4%
5722 2941
 
0.4%
5673 2855
 
0.4%
5578 2840
 
0.4%
5511 2835
 
0.4%
Other values (921) 697929
95.1%
ValueCountFrequency (%)
1 161
 
< 0.1%
2 389
0.1%
3 573
0.1%
4 602
0.1%
5 758
0.1%
ValueCountFrequency (%)
10846 5523
0.8%
9717 4842
0.7%
8715 4384
0.6%
6864 3466
0.5%
6270 3179
0.4%

days_since_pdv_first_activation
Real number (ℝ)

Zeros 

Distinct90
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.20767636
Minimum0
Maximum89
Zeros15549
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:38.536551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q120
median41
Q364
95-th percentile84
Maximum89
Range89
Interquartile range (IQR)44

Descriptive statistics

Standard deviation25.66268689
Coefficient of variation (CV)0.6080099429
Kurtosis-1.141257205
Mean42.20767636
Median Absolute Deviation (MAD)22
Skewness0.1104147927
Sum30974652
Variance658.5734987
MonotonicityNot monotonic
2025-04-28T01:04:38.641495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15549
 
2.1%
22 12280
 
1.7%
50 12128
 
1.7%
21 11796
 
1.6%
49 11218
 
1.5%
51 11059
 
1.5%
80 10264
 
1.4%
81 10166
 
1.4%
48 10089
 
1.4%
43 10066
 
1.4%
Other values (80) 619248
84.4%
ValueCountFrequency (%)
0 15549
2.1%
1 7890
1.1%
2 6295
0.9%
3 8521
1.2%
4 8300
1.1%
ValueCountFrequency (%)
89 4932
0.7%
88 7058
1.0%
87 8462
1.2%
86 5966
0.8%
85 6978
1.0%

time_phase_code
Real number (ℝ)

Zeros 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.193196823
Minimum0
Maximum3
Zeros127073
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:38.704954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7136360006
Coefficient of variation (CV)0.5980874127
Kurtosis-0.9116410557
Mean1.193196823
Median Absolute Deviation (MAD)1
Skewness-0.2418905358
Sum875643
Variance0.5092763414
MonotonicityNot monotonic
2025-04-28T01:04:38.785426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
1 340539
46.4%
2 263649
35.9%
0 127073
 
17.3%
3 2602
 
0.4%
ValueCountFrequency (%)
0 127073
 
17.3%
1 340539
46.4%
2 263649
35.9%
3 2602
 
0.4%
ValueCountFrequency (%)
3 2602
 
0.4%
2 263649
35.9%
1 340539
46.4%
0 127073
 
17.3%

subscriber_sim_count
Real number (ℝ)

Skewed 

Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.291136084
Minimum1
Maximum1745
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:38.850159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum1745
Range1744
Interquartile range (IQR)1

Descriptive statistics

Standard deviation61.95629085
Coefficient of variation (CV)14.43820229
Kurtosis766.9002614
Mean4.291136084
Median Absolute Deviation (MAD)0
Skewness27.53600142
Sum3149106
Variance3838.581975
MonotonicityNot monotonic
2025-04-28T01:04:39.285210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 414755
56.5%
2 145786
 
19.9%
3 98912
 
13.5%
4 37272
 
5.1%
5 18327
 
2.5%
6 8843
 
1.2%
7 3235
 
0.4%
8 1504
 
0.2%
1745 905
 
0.1%
9 725
 
0.1%
Other values (47) 3599
 
0.5%
ValueCountFrequency (%)
1 414755
56.5%
2 145786
 
19.9%
3 98912
 
13.5%
4 37272
 
5.1%
5 18327
 
2.5%
ValueCountFrequency (%)
1745 905
0.1%
500 264
 
< 0.1%
120 68
 
< 0.1%
102 50
 
< 0.1%
92 46
 
< 0.1%

pdv_total_activations
Real number (ℝ)

Distinct931
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1133.254459
Minimum1
Maximum10846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:39.383029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1131
median440
Q31349
95-th percentile5350
Maximum10846
Range10845
Interquartile range (IQR)1218

Descriptive statistics

Standard deviation1796.797711
Coefficient of variation (CV)1.585520089
Kurtosis10.75911886
Mean1133.254459
Median Absolute Deviation (MAD)377
Skewness3.062936257
Sum831653517
Variance3228482.014
MonotonicityNot monotonic
2025-04-28T01:04:39.498871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10846 5523
 
0.8%
9717 4842
 
0.7%
8715 4384
 
0.6%
6864 3466
 
0.5%
6270 3179
 
0.4%
5925 3069
 
0.4%
5722 2941
 
0.4%
5673 2855
 
0.4%
5578 2840
 
0.4%
5511 2835
 
0.4%
Other values (921) 697929
95.1%
ValueCountFrequency (%)
1 161
 
< 0.1%
2 389
0.1%
3 573
0.1%
4 602
0.1%
5 758
0.1%
ValueCountFrequency (%)
10846 5523
0.8%
9717 4842
0.7%
8715 4384
0.6%
6864 3466
0.5%
6270 3179
0.4%

pdv_unique_customers
Real number (ℝ)

Distinct821
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean840.2472355
Minimum1
Maximum6726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:39.616479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27
Q1112
median354
Q31097
95-th percentile3428
Maximum6726
Range6725
Interquartile range (IQR)985

Descriptive statistics

Standard deviation1236.895147
Coefficient of variation (CV)1.472060953
Kurtosis8.475910123
Mean840.2472355
Median Absolute Deviation (MAD)295
Skewness2.76374274
Sum616626357
Variance1529909.604
MonotonicityNot monotonic
2025-04-28T01:04:39.699470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6726 5523
 
0.8%
6339 4842
 
0.7%
6199 4384
 
0.6%
5938 3466
 
0.5%
3860 3179
 
0.4%
3842 3069
 
0.4%
4343 2941
 
0.4%
4533 2855
 
0.4%
3894 2840
 
0.4%
3428 2835
 
0.4%
Other values (811) 697929
95.1%
ValueCountFrequency (%)
1 233
 
< 0.1%
2 430
0.1%
3 644
0.1%
4 675
0.1%
5 859
0.1%
ValueCountFrequency (%)
6726 5523
0.8%
6339 4842
0.7%
6199 4384
0.6%
5938 3466
0.5%
4533 2855
0.4%

pdv_avg_sims_per_customer
Real number (ℝ)

Skewed 

Distinct2634
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.299711143
Minimum1
Maximum29.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:39.783116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.025672372
Q11.091370558
median1.192307692
Q31.328358209
95-th percentile1.60764294
Maximum29.6
Range28.6
Interquartile range (IQR)0.2369876506

Descriptive statistics

Standard deviation1.288224596
Coefficient of variation (CV)0.9911622309
Kurtosis438.9347381
Mean1.299711143
Median Absolute Deviation (MAD)0.114959626
Skewness20.48589947
Sum953809.9186
Variance1.65952261
MonotonicityNot monotonic
2025-04-28T01:04:39.890325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 24230
 
3.3%
1.61254832 5523
 
0.8%
1.532891623 4842
 
0.7%
1.405871915 4384
 
0.6%
1.155944763 3466
 
0.5%
1.624352332 3179
 
0.4%
1.542165539 3069
 
0.4%
1.25 2958
 
0.4%
1.31752245 2941
 
0.4%
1.25148908 2855
 
0.4%
Other values (2624) 676416
92.2%
ValueCountFrequency (%)
1 24230
3.3%
1.006896552 75
 
< 0.1%
1.007751938 58
 
< 0.1%
1.008196721 56
 
< 0.1%
1.00862069 56
 
< 0.1%
ValueCountFrequency (%)
29.6 1370
0.2%
16.16666667 158
 
< 0.1%
15.4 32
 
< 0.1%
14.72222222 139
 
< 0.1%
8.516129032 262
 
< 0.1%

pdv_multi_sim_pct
Real number (ℝ)

Zeros 

Distinct2314
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.23109174
Minimum0
Maximum100
Zeros24230
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:39.982363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.43902439
Q17.407407407
median14.72184532
Q323.18181818
95-th percentile35.26803708
Maximum100
Range100
Interquartile range (IQR)15.77441077

Descriptive statistics

Standard deviation10.94128893
Coefficient of variation (CV)0.6740944541
Kurtosis2.73558275
Mean16.23109174
Median Absolute Deviation (MAD)7.809749151
Skewness1.101034708
Sum11911397.68
Variance119.7118034
MonotonicityNot monotonic
2025-04-28T01:04:40.106691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24230
 
3.3%
36.60422242 5523
 
0.8%
33.12825367 4842
 
0.7%
29.26278432 4384
 
0.6%
12.86628494 3466
 
0.5%
10 3267
 
0.4%
36.16580311 3179
 
0.4%
34.64341489 3069
 
0.4%
23.07160949 2941
 
0.4%
19.19258769 2855
 
0.4%
Other values (2304) 676107
92.1%
ValueCountFrequency (%)
0 24230
3.3%
0.6896551724 75
 
< 0.1%
0.7751937984 58
 
< 0.1%
0.8196721311 56
 
< 0.1%
0.8474576271 59
 
< 0.1%
ValueCountFrequency (%)
100 95
 
< 0.1%
91.66666667 13
 
< 0.1%
80 32
 
< 0.1%
77.77777778 297
< 0.1%
76.69565217 734
0.1%

reconstruction_error
Real number (ℝ)

Skewed 

Distinct424372
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.803216174
Minimum0.0001709916412
Maximum5744.21179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-04-28T01:04:40.195295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0001709916412
5-th percentile0.003809096105
Q10.00916532779
median0.01625324746
Q30.02771710227
95-th percentile0.07940948116
Maximum5744.21179
Range5744.211619
Interquartile range (IQR)0.01855177448

Descriptive statistics

Standard deviation189.7961793
Coefficient of variation (CV)27.89800801
Kurtosis806.0726272
Mean6.803216174
Median Absolute Deviation (MAD)0.008328934938
Skewness28.40803767
Sum4992628.631
Variance36022.58968
MonotonicityNot monotonic
2025-04-28T01:04:40.297187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5365.131941 266
 
< 0.1%
5358.788618 139
 
< 0.1%
296.6560894 105
 
< 0.1%
5358.612766 99
 
< 0.1%
5362.388097 87
 
< 0.1%
5361.305609 62
 
< 0.1%
295.9920249 61
 
< 0.1%
5364.834484 59
 
< 0.1%
5703.806651 53
 
< 0.1%
5744.21179 53
 
< 0.1%
Other values (424362) 732879
99.9%
ValueCountFrequency (%)
0.0001709916412 1
 
< 0.1%
0.0002323129427 1
 
< 0.1%
0.0002436047305 2
< 0.1%
0.0002756425174 1
 
< 0.1%
0.0002999267554 3
< 0.1%
ValueCountFrequency (%)
5744.21179 53
 
< 0.1%
5703.806651 53
 
< 0.1%
5365.131941 266
< 0.1%
5364.834484 59
 
< 0.1%
5362.814672 27
 
< 0.1%

is_anomaly
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.8 KiB
False
722262 
True
 
11601
ValueCountFrequency (%)
False 722262
98.4%
True 11601
 
1.6%
2025-04-28T01:04:40.366097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Sample

msisdnactivation_datesubscriber_wilayasubscriber_id_numberpdv_idpdv_wilayastatuscustomer_typefirst_cdr_wilayasubscriber_wilaya_stdpdv_wilaya_stdsubscriber_wilaya_codepdv_wilaya_codeactivation_houractivation_dayofweekactivation_monthactivation_weektime_phasesame_wilayawilaya_code_diffpdv_activation_countdays_since_pdv_first_activationtime_phase_codesubscriber_sim_countpdv_total_activationspdv_unique_customerspdv_avg_sims_per_customerpdv_multi_sim_pctreconstruction_erroris_anomaly
03000100252025-01-29 10:56:38ALGER069182991590951419D48AlgiersActiveINDIVIDUAL?AlgerAlger16.01610315Morning10.077921017797071.1018398.7694480.008248False
13000100382025-03-06 11:18:09OULED-DJELLAL59048923899923990P34752107-BISKRAActiveINDIVIDUAL?Ouled DjellalBiskra51.07114310Morning044.023655701236516531.43073229.2800970.050278False
23000100522025-03-14 22:48:23CONSTANTINE109778991590927189P66556525-CONSTANTINEActiveINDIVIDUAL?ConstantineConstantine25.025225311Evening10.015815321158111261.40408528.8632330.018609False
33000100752025-02-22 11:54:44OUARGLA715903991550999329P96663030-OUARGLAActiveINDIVIDUAL?OuarglaOuargla30.03011628Morning10.029410129291.0000000.0000000.037596False
43000100822025-02-02 14:36:33BORDJ-BOU-ARRERIDJ965546992559917639P95453334-BORDJ-BOU-ARRERIDJActiveINDIVIDUAL?Bordj Bou ArreridjBordj Bou Arreridj34.03414025Afternoon10.037424113743131.19488815.6549520.011609False
msisdnactivation_datesubscriber_wilayasubscriber_id_numberpdv_idpdv_wilayastatuscustomer_typefirst_cdr_wilayasubscriber_wilaya_stdpdv_wilaya_stdsubscriber_wilaya_codepdv_wilaya_codeactivation_houractivation_dayofweekactivation_monthactivation_weektime_phasesame_wilayawilaya_code_diffpdv_activation_countdays_since_pdv_first_activationtime_phase_codesubscriber_sim_countpdv_total_activationspdv_unique_customerspdv_avg_sims_per_customerpdv_multi_sim_pctreconstruction_erroris_anomaly
7338583497405742025-01-19 17:23:39OUARGLA859385291290995259D28OuarglaActiveINDIVIDUAL?OuarglaOuargla30.03017013Afternoon10.063511116356071.0461294.2833610.016321False
7338593497406022025-01-20 20:09:36TOUGGOURT825591998590925279P66997930-OUARGLAActiveINDIVIDUAL?TouggourtOuargla55.03020114Evening025.01632116161.0000000.0000000.030959False
7338603497406082025-02-17 19:09:34CHLEF089949999290995439P73653642-TIPAZAActiveINDIVIDUALTIPAZAChlefTipaza2.04219128Evening040.026840222682071.29468622.2222220.052666False
7338613497406122025-02-25 15:09:13ORAN875559994520989549P17394031-ORANActiveINDIVIDUAL?OranOran31.03115229Afternoon10.035948123593361.0684525.9523810.006285False
7338623497406212025-01-08 11:45:10SKIKDA099841995590922199P78569241-SOUK-AHRASActiveINDIVIDUAL?SkikdaSouk Ahras21.04111312Morning020.04600146431.0697676.9767440.023883False